1. Field of the Art
The disclosure relates to the field of speech analytics, and more particularly to the field of analyzing speech patterns using language-independent, non-semantic speech analytics.
2. Discussion of the State of the Art
In the field of speech analytics, much progress has been made in recognizing and processing natural spoken language effectively. However, current approaches focus on language-specific analytics, such as processing for words, phrases, or phonemes that are tied to a particular world language.
There are some efforts to monitor or interpret speech patterns without regard for the content being spoken (thus, a language-independent approach), but the current methods focus on speech analysis only insofar as monitoring simple metrics such as speech rate of a telephone call participant, or comparing a speaker's voice patterns when calm against patterns when emotional, such as to determine when they are in an emotional state (such as for truth detection, for example). These approaches offer only a simplistic approach to speech analytics, measuring a single metric or focusing on a specific speaker or use case rather than answering a broader need for language-independent speech analytics in general.
What is needed is a means to analyze speech using means that are not tied to any specific language, instead focusing on non-semantic elements of spoken language, and that may be applied broadly to existing or novel speech analysis systems or hardware and in various use cases as needed.